Data Scientist

SoluGrowth (Pty) Ltd.; Gauteng; On-site; ZAR 30,000 - 70,000; Full time

Job summary

Join a dynamic financial consulting team as a Data Scientist, where your analytical skills will shine. In this role, you will tackle complex financial challenges by collecting and interpreting large datasets, utilizing advanced statistical techniques, and applying machine learning algorithms. Collaborate with talented professionals to create innovative solutions that drive impactful business decisions. Your expertise in Python, R, and SAS will be crucial in developing predictive models that forecast risk and customer behavior. This is an exciting opportunity to contribute to a forward-thinking company that values data-driven insights.

Qualifications

• Proven experience in data analysis and machine learning.
• Strong understanding of statistical techniques and data cleaning.

Responsibilities

• Analyze large datasets to extract valuable insights for financial decisions.
• Develop predictive models to forecast financial metrics.

Skills

Data Analysis

Statistical Techniques

Machine Learning

Python

R

SAS

Data Cleaning

Exploratory Data Analysis

Education

Bachelor's Degree in Data Science or related field

Master's Degree in Data Science or related field

Tools

Python

R

SAS

Job description

Data Scientist Job Description

We are seeking a skilled Data Scientist to join our financial consulting team. In this role, you will:

1. Collect, analyze, and interpret large datasets to extract valuable insights.
2. Leverage your analytical skills and technical expertise to solve complex financial challenges and contribute to impactful business decisions.
3. Apply data science techniques to real-world financial problems and contribute to innovative solutions.
4. Collaborate with talented team members across various departments to add value to clients.
5. Clean, prepare, and analyze large datasets from sources such as banks, insurance companies, and investment firms.
6. Conduct exploratory data analysis to identify patterns, trends, and anomalies.
7. Utilize statistical techniques like regression and hypothesis testing to assess relationships between variables.
8. Use Python, R, and SAS to analyze data, extract insights, and present findings to stakeholders.
9. Work with cross-functional teams to integrate data science solutions into business strategies and workflows.
10. Develop and implement predictive models, including machine learning algorithms, to forecast financial metrics such as risk, returns, and customer behavior.
11. Evaluate model performance and refine models to improve accuracy and effectiveness.